The impact of lecture capture availability on academic performance in a large biomedical science course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lecture capture is a technology where live lectures are recorded in a digital format and made available to students to view at their convenience. The use of this technology in higher education has steadily increased despite mixed results as to whether it is beneficial to student achievement. The current study utilized a two-group quasi-experimental design to examine the impact of lecture capture availability on academic performance in a large enrollment, two-term, second year biomedical science course. Academic performance was compared between two matched cohorts enrolled in the same biomedical science course taught by the same instructor in which one course did not have access to lecture recordings (2017–18 academic year, N = 433) and the other did (2018–19 academic year, N = 414). Academic performance was evaluated by comparing scores on identical exam questions and the final grade earned in the course. Student’s t-test revealed that lecture capture availability resulted in a decline in performance on exams and the final course grade. We also evaluated whether lecture capture influenced student attendance via an in-class student response system and a t-test found that student attendance was comparable between the cohorts. A chi-squared test also found that lecture capture availability resulted in significantly more course failures. Importantly, a student’s t-test showed that GPA did not differ between the cohorts. To our knowledge this is the first study to show that lecture capture availability resulted in a decline in academic performance despite similar in-class attendance and GPA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it